[MXNET-1041] Add Java benchmark

scala-package/examples/scripts/benchmark/run_java_inference_bm.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+set -e
+
+hw_type=cpu
+if [ "$USE_GPU" = "1" ]
+then
+    hw_type=gpu
+fi
+
+platform=linux-x86_64
+
+if [[ $OSTYPE = [darwin]* ]]
+then
+    platform=osx-x86_64
+fi
+
+MXNET_ROOT=$(cd "$(dirname $0)/../../../.."; pwd)
+CLASS_PATH=$MXNET_ROOT/scala-package/assembly/$platform-$hw_type/target/*:$MXNET_ROOT/scala-package/examples/target/*
+
+java -Xmx8G -Dmxnet.traceLeakedObjects=true -cp $CLASS_PATH \
+	org.apache.mxnetexamples.javaapi.benchmark.JavaBenchmark $@
+

scala-package/examples/scripts/infer/objectdetector/run_ssd_java_example.sh

@@ -41,7 +41,7 @@ INPUT_IMG=$2
 INPUT_DIR=$3
 
 java -Xmx8G -cp $CLASS_PATH \
-	org.apache.mxnetexamples.infer.javapi.objectdetector.SSDClassifierExample \
+	org.apache.mxnetexamples.javaapi.infer.objectdetector.SSDClassifierExample \
 	--model-path-prefix $MODEL_DIR \
 	--input-image $INPUT_IMG \
 	--input-dir $INPUT_DIR

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/benchmark/InferBase.java

@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.mxnetexamples.javaapi.benchmark;
+
+import org.apache.mxnet.javaapi.Context;
+import org.kohsuke.args4j.Option;
+
+import java.util.List;
+
+public abstract class InferBase {
+    @Option(name = "--num-runs", usage = "Number of runs")
+    public int numRun = 1;
+    @Option(name = "--model-name", usage = "Name of the model")
+    public String modelName = "";
+    @Option(name = "--batchsize", usage = "Size of the batch")
+    public int batchSize = 1;
+
+    public abstract void preProcessModel(List<Context> context);
+    public abstract void runSingleInference();
+    public abstract void runBatchInference();
+}

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/benchmark/JavaBenchmark.java

@@ -0,0 +1,124 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnetexamples.javaapi.benchmark;
+
+import org.apache.mxnet.javaapi.Context;
+import org.kohsuke.args4j.CmdLineParser;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+public class JavaBenchmark {
+
+    private static boolean runBatch = false;
+
+    private static void parse(Object inst, String[] args) {
+        CmdLineParser parser  = new CmdLineParser(inst);
+        try {
+            parser.parseArgument(args);
+        } catch (Exception e) {
+            System.err.println(e.getMessage() + e);
+            parser.printUsage(System.err);
+            System.exit(1);
+        }
+    }
+
+    private static long percentile(int p, long[] seq) {
+        Arrays.sort(seq);
+        int k = (int) Math.ceil((seq.length - 1) * (p / 100.0));
+        return seq[k];
+    }
+
+    private static void printStatistics(long[] inferenceTimes, String metricsPrefix)  {
+
+        double p50 = percentile(50, inferenceTimes) / 1.0e6;
+        double p99 = percentile(99, inferenceTimes) / 1.0e6;
+        double p90 = percentile(90, inferenceTimes) / 1.0e6;
+        long sum = 0;
+        for (long time: inferenceTimes) sum += time;
+        double average = sum / (inferenceTimes.length * 1.0e6);
+
+        System.out.println(
+                String.format("\n%s_p99 %fms\n%s_p90 %fms\n%s_p50 %fms\n%s_average %1.2fms",
+                        metricsPrefix, p99, metricsPrefix, p90,
+                        metricsPrefix, p50, metricsPrefix, average)
+        );
+
+    }
+
+    private static List<Context> getContext()  {
+        List<Context> context = new ArrayList<Context>();
+        if (System.getenv().containsKey("SCALA_TEST_ON_GPU") &&
+                Integer.valueOf(System.getenv("SCALA_TEST_ON_GPU")) == 1) {
+            context.add(Context.gpu());
+        } else {
+            context.add(Context.cpu());
+        }
+        return context;
+    }
+
+    public static void main(String[] args) {
+        if (args.length < 2) {
+            System.out.println("Please specify model name");
+            return;
+        }
+        String modelName = args[1];
+        InferBase model;
+        switch(modelName) {
+            case "ObjectDetection":
+                runBatch = true;
+                ObjectDetectionBenchmark inst = new ObjectDetectionBenchmark();
+                parse(inst, args);
+                model = inst;
+            default:
+                System.err.println("Model name not found! " + modelName);
+                System.exit(1);
+        }
+        List<Context> context = getContext();
+        if (System.getenv().containsKey("SCALA_TEST_ON_GPU") &&
+                Integer.valueOf(System.getenv("SCALA_TEST_ON_GPU")) == 1) {
+            context.add(Context.gpu());
+        } else {
+            context.add(Context.cpu());
+        }
+
+        long[] result = new long[model.numRun];
+        model.preProcessModel(context);
+        if (runBatch) {
+            for (int i =0;i < model.numRun; i++) {
+                long currTime = System.nanoTime();
+                model.runBatchInference();
+                result[i] = System.nanoTime() - currTime;
+            }
+            printStatistics(result, modelName +"Batch");
+        }
+
+        model.batchSize = 1;
+        model.preProcessModel(context);
+        result = new long[model.numRun];
+        for (int i = 0; i < model.numRun; i++) {
+            long currTime = System.nanoTime();
+            model.runSingleInference();
+            result[i] = System.nanoTime() - currTime;
+        }
+        printStatistics(result, modelName);
+    }
+}

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/benchmark/ObjectDetectionBenchmark.java

@@ -0,0 +1,64 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnetexamples.javaapi.benchmark;
+
+import org.apache.mxnet.infer.javaapi.ObjectDetector;
+import org.apache.mxnet.javaapi.*;
+import org.kohsuke.args4j.Option;
+
+import java.util.ArrayList;
+import java.util.List;
+
+public class ObjectDetectionBenchmark extends InferBase {
+    @Option(name = "--model-path-prefix", usage = "input model directory and prefix of the model")
+    public String modelPathPrefix = "/model/ssd_resnet50_512";
+    @Option(name = "--input-image", usage = "the input image")
+    public String inputImagePath = "/images/dog.jpg";
+
+    private ObjectDetector objDet;
+    private NDArray img;
+    private NDArray$ NDArray = NDArray$.MODULE$;
+
+    public void preProcessModel(List<Context> context) {
+        Shape inputShape = new Shape(new int[] {this.batchSize, 3, 512, 512});
+        List<DataDesc> inputDescriptors = new ArrayList<>();
+        inputDescriptors.add(new DataDesc("data", inputShape, DType.Float32(), "NCHW"));
+        objDet = new ObjectDetector(modelPathPrefix, inputDescriptors, context, 0);
+        img = ObjectDetector.bufferedImageToPixels(
+                ObjectDetector.reshapeImage(
+                    ObjectDetector.loadImageFromFile(inputImagePath), 512, 512
+                ),
+                new Shape(new int[] {1, 3, 512, 512})
+        );
+    }
+
+    public void runSingleInference() {
+        List<NDArray> nd = new ArrayList<>();
+        nd.add(img);
+        objDet.objectDetectWithNDArray(nd, 3);
+    }
+
+    public void runBatchInference() {
+        List<NDArray> nd = new ArrayList<>();
+        NDArray[] temp = new NDArray[batchSize];
+        for (int i = 0; i < batchSize; i++) temp[i] = img.copy();
+        NDArray batched = NDArray.concat(temp, batchSize).setdim(0).invoke().get();
+        nd.add(batched);
+        objDet.objectDetectWithNDArray(nd, 3);
+    }
+}

scala-package/examples/src/main/java/org/apache/mxnetexamples/infer/javapi/objectdetector/SSDClassifierExample.java → scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/objectdetector/SSDClassifierExample.java

@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.mxnetexamples.infer.javapi.objectdetector;
+package org.apache.mxnetexamples.javaapi.infer.objectdetector;
 
 import org.apache.mxnet.infer.javaapi.ObjectDetectorOutput;
 import org.kohsuke.args4j.CmdLineParser;

scala-package/infer/src/main/scala/org/apache/mxnet/infer/javaapi/ObjectDetector.scala

@@ -19,6 +19,8 @@ package org.apache.mxnet.infer.javaapi
 
 // scalastyle:off
 import java.awt.image.BufferedImage
+
+import org.apache.mxnet.javaapi.Shape
 // scalastyle:on
 
 import org.apache.mxnet.javaapi.{Context, DataDesc, NDArray}
@@ -113,6 +115,14 @@ object ObjectDetector {
     org.apache.mxnet.infer.ImageClassifier.loadImageFromFile(inputImagePath)
   }
 
+  def reshapeImage(img : BufferedImage, newWidth: Int, newHeight: Int): BufferedImage = {
+    org.apache.mxnet.infer.ImageClassifier.reshapeImage(img, newWidth, newHeight)
+  }
+
+  def bufferedImageToPixels(resizedImage: BufferedImage, inputImageShape: Shape): NDArray = {
+    org.apache.mxnet.infer.ImageClassifier.bufferedImageToPixels(resizedImage, inputImageShape)
+  }
+
   def loadInputBatch(inputImagePaths: java.util.List[String]): java.util.List[BufferedImage] = {
     org.apache.mxnet.infer.ImageClassifier
       .loadInputBatch(inputImagePaths.asScala.toList).toList.asJava